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Volatility Modeling of Stocks from Selected Sectors of the Indian Economy Using GARCH

arXiv 2105.13898 ↗First posted 28 May 2021q-fin.CP cs.LG q-fin.STcited by 12

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Abstract (arXiv)

Volatility clustering is an important characteristic that has a significant effect on the behavior of stock markets. However, designing robust models for accurate prediction of future volatilities of stock prices is a very challenging research problem. We present several volatility models based on generalized autoregressive conditional heteroscedasticity (GARCH) framework for modeling the volatility of ten stocks listed in the national stock exchange (NSE) of India. The stocks are selected from the auto sector and the banking sector of the Indian economy, and they have a significant impact on the sectoral index of their respective sectors in the NSE. The historical stock price records from Jan 1, 2010, to Apr 30, 2021, are scraped from the Yahoo Finance website using the DataReader API of the Pandas module in the Python programming language. The GARCH modules are built and fine-tuned on the training data and then tested on the out-of-sample data to evaluate the performance of the models. The analysis of the results shows that asymmetric GARCH models yield more accurate forecasts on the future volatility of stocks.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 2 of its 12 references that are in the map. First posted on arXiv 28 May 2021.

Builds on

2 of 12 references.

References · 2

  1. 2020Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Modelscited by 24
  2. 2021Profitability Analysis in Stock Investment Using an LSTM-Based Deep Learning Modelcited by 11

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Cited by year

Citing papers per year: 2021: 3; 2022: 5; 2023: 3; 2024: 1.

12 papers.

  1. 2021Stock Portfolio Optimization Using a Deep Learning LSTM Modelcited by 9
  2. 2022Robust Portfolio Design and Stock Price Prediction Using an Optimized LSTM Modelcited by 6
  3. 2022Hierarchical Risk Parity and Minimum Variance Portfolio Design on NIFTY 50 Stockscited by 4
  4. 2021Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Modelcited by 3
  5. 2022A Comparative Study of Hierarchical Risk Parity Portfolio and Eigen Portfolio on the NIFTY 50 Stockscited by 3
  6. 2021Optimum Risk Portfolio and Eigen Portfolio: A Comparative Analysis Using Selected Stocks from the Indian Stock Marketcited by 2
  7. 2022Stock Volatility Prediction using Time Series and Deep Learning Approachcited by 2
  8. 2022DNN-ForwardTesting: A New Trading Strategy Validation using Statistical Timeseries Analysis and Deep Neural Networkscited by 2
  9. 2023A Comparative Analysis of Portfolio Optimization Using Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian Stock Marketcited by 0
  10. 2023Portfolio Optimization: A Comparative Studycited by 0
  11. 2023Performance Evaluation of Equal-Weight Portfolio and Optimum Risk Portfolio on Indian Stockscited by 0
  12. 2024Exploring Sectoral Profitability in the Indian Stock Market Using Deep Learningcited by 0